@InProceedings{sikdar-gamback:2016:WNUT,
  author    = {Sikdar, Utpal Kumar  and  Gamb\"{a}ck, Bj\"{o}rn},
  title     = {Feature-Rich Twitter Named Entity Recognition and Classification},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {164--170},
  abstract  = {Twitter named entity recognition is the process of identifying proper names and
	classifying them into some predefined labels/categories. The paper introduces a
	Twitter named entity system using a supervised machine learning approach,
	namely Conditional Random Fields. A large set of different features was
	developed and the system was trained using these. The Twitter named entity task
	can be divided into two parts: i) Named entity extraction from tweets and ii)
	Twitter name classification into ten different types. For Twitter named entity
	recognition on unseen test data, our system obtained the second highest F1
	score in the shared task: 63.22%. The system performance on the classification
	task was worse, with an F1 measure of 40.06% on unseen test data, which was the
	fourth best of the ten systems participating in the shared task.},
  url       = {http://aclweb.org/anthology/W16-3922}
}